20 research outputs found
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Delivering treatment recommendations via pervasive electronic devices such as
mobile phones has the potential to be a viable and scalable treatment medium
for long-term health behavior management. But active experimentation of
treatment options can be time-consuming, expensive and altogether unethical in
some cases. There is a growing interest in methodological approaches that allow
an experimenter to learn and evaluate the usefulness of a new treatment
strategy before deployment. We present the first development of a treatment
recommender system for emotion regulation using real-world historical mobile
digital data from n = 114 high socially anxious participants to test the
usefulness of new emotion regulation strategies. We explore a number of offline
contextual bandits estimators for learning and propose a general framework for
learning algorithms. Our experimentation shows that the proposed doubly robust
offline learning algorithms performed significantly better than baseline
approaches, suggesting that this type of recommender algorithm could improve
emotion regulation. Given that emotion regulation is impaired across many
mental illnesses and such a recommender algorithm could be scaled up easily,
this approach holds potential to increase access to treatment for many people.
We also share some insights that allow us to translate contextual bandit models
to this complex real-world data, including which contextual features appear to
be most important for predicting emotion regulation strategy effectiveness.Comment: Accepted at RecSys 202
Dynamic Resting-State Functional Connectivity in Major Depression
Major depressive disorder (MDD) is characterized by abnormal resting-state functional connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default network. However, prior research in MDD has not examined dynamic changes in functional connectivity as networks form, interact, and dissolve over time. We compared unmedicated individuals with MDD (n=100) to control participants (n=109) on dynamic RSFC (operationalized as SD in RSFC over a series of sliding windows) of an MPFC seed region during a resting-state functional magnetic resonance imaging scan. Among participants with MDD, we also investigated the relationship between symptom severity and RSFC. Secondary analyses probed the association between dynamic RSFC and rumination. Results showed that individuals with MDD were characterized by decreased dynamic (less variable) RSFC between MPFC and regions of parahippocampal gyrus within the default network, a pattern related to sustained positive connectivity between these regions across sliding windows. In contrast, the MDD group exhibited increased dynamic (more variable) RSFC between MPFC and regions of insula, and higher severity of depression was related to increased dynamic RSFC between MPFC and dorsolateral prefrontal cortex. These patterns of highly variable RSFC were related to greater frequency of strong positive and negative correlations in activity across sliding windows. Secondary analyses indicated that increased dynamic RSFC between MPFC and insula was related to higher levels of recent rumination. These findings provide initial evidence that depression, and ruminative thinking in depression, are related to abnormal patterns of fluctuating communication among brain systems involved in regulating attention and self-referential thinking
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Resting state brain dynamics: Associations with childhood sexual abuse and major depressive disorder
Early life stress (ELS) and major depressive disorder (MDD) share neural network abnormalities. However, it is unclear how ELS and MDD may separately and/or jointly relate to brain networks, and whether neural differences exist between depressed individuals with vs without ELS. Moreover, prior work evaluated static versus dynamic network properties, a critical gap considering brain networks show changes in coordinated activity over time. Seventy-one unmedicated females with and without childhood sexual abuse (CSA) histories and/or MDD completed a resting state scan and a stress task in which cortisol and affective ratings were collected. Recurring functional network co-activation patterns (CAPs) were examined and time in CAP (number of times each CAP is expressed) and transition frequencies (transitioning between different CAPs) were computed. The effects of MDD and CSA on CAP metrics were examined and CAP metrics were correlated with depression and stress-related variables. Results showed that MDD, but not CSA, related to CAP metrics. Specifically, individuals with MDD (N = 35) relative to HCs (N = 36), spent more time in a posterior default mode (DMN)-frontoparietal network (FPN) CAP and transitioned more frequently between posterior DMN-FPN and prototypical DMN CAPs. Across groups, more time spent in a posterior DMN-FPN CAP and greater DMN-FPN and prototypical DMN CAP transition frequencies were linked to higher rumination. Imbalances between the DMN and the FPN appear central to MDD and might contribute to MDD-related cognitive dysfunction, including rumination. Unexpectedly, CSA did not modulate such dysfunctions, a finding that needs to be replicated by future studies with larger sample sizes.
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